Various types of X-ray imaging offer tools for diagnostic assessment of patient health. Conventional x-ray imaging captures individual, two-dimensional (2D) images of patient anatomy. Volume imaging modalities obtain multiple images in sequence to provide additional depth information, allowing imaging of internal anatomy with three-dimensional (3D) views displayed from different angles and with adjustable sectioning for viewing slices of the volume data, thus allowing the view of internal structures and organs of the patient at different depths from the skin surface.
One known difficulty with X-ray imaging in both 2D imaging modalities (such as radiography, fluoroscopy, or mammography, and the like) and 3D imaging modalities (such as computed tomography CT, multi-detector computed tomography MDCT, cone beam computed tomography CBCT, tomosynthesis, dual energy CT, or spectral CT, and the like) relates to the adverse impact of accumulated effects of scattered radiation on image quality. Scatter itself results from secondary, randomized effects of interaction of the radiation energy with the irradiated tissue. Scatter occurs when radiation from the x-ray source reaches a detector by an indirect path that can extend into material that lies outside the field of view. The primary X-ray beam is directed towards and bombards the sample with some of the X-ray radiation being absorbed, a smaller amount being scattered, and the remainder continuing on to the detector. Scatter is known to contribute to noise and low contrast in the projection images and can substantially reduce image quality and introduce artifacts into the x-ray image and, as a result, into any reconstructed volume images.
Scatter can be modeled probabilistically and compensation for scatter can be applied in the same manner, helping to reduce the effects of scatter on the image content that is acquired. Scatter from the scanned subject can be modeled using information about the X-ray source (such as spectral information, filters, and exposure distribution), about detector response, and also about the materials that lie within the irradiated field of view. Thus, scatter compensation can take advantage of information about the anatomy that receives the radiation. In order to more accurately compensate for scatter, it is further useful to provide ways to model scatter that results from interaction of the radiated energy with materials in the volume that lies outside the field of view.
Monte Carlo simulation is a calculation tool that is widely used to model scatter behavior in radiographic imaging. Advantaged over alternate calculation approaches, Monte Carlo techniques have been shown to be useful in helping to solve complex stochastic problems such as radiation scatter, wherein random particle behavior can be evaluated with respect to a probability function. Monte Carlo implementation, however, requires considerable calculation time and resources. Modeling scatter behavior for a conventional 2D radiographic image using Monte Carlo analysis can take a number of hours; this calculation time increases substantially when handling volume 3D images.
Thus, it can be appreciated that methods that streamline the Monte Carlo calculation process can be of considerable value in improving the efficiency of scatter estimation and consequent scatter correction for radiographic images.